A big 6-foot K&E sliderule hung at the front of my high-school chemistry classroom, but was never used. At graduation ('91) I asked the teacher if I could have the slide rule and she said "sure".
I keep it now in my office, and once a year I bring to the data visualization class I teach at UChicago, to show how it works, and to show it as an example of a visual device in aid of computational thinking (nomographs being another great example).
I love the Vernier scales! But I honestly hadn't known about the sense in which slide rules have a Venier scale until your comment, which led me to [1]. In another class on laser-cutting I had students make a kerf-meter, which became more accurate with a Vernier scale.
I learned about this not from Mathologer, but Numberphile [1]. The second half of the video is the continued fraction derivation. I remember this being the first time I appreciated the sense in which the phi was the most irrational number, which otherwise seemed like just a click-bait-y idea. But you've found an earlier (9 years ago vs 7) Mathologer video on the same topic.
It may have jumped the shark, but it may be that now there's space for actual experimentation and innovation again. This talk from Scott Jenson (who worked at Apple in Human Interfaces) was thought-provoking and gave me a little optimism: https://www.youtube.com/watch?v=1fZTOjd_bOQ
The website says "I basically need to design puzzles in reverse and have built a set of tools to help me with that." Does that mean that each day's puzzle is essentially built by hand? It seems like an interesting and non-trivial search problem to automatically generate puzzles, given a dictionary.
Uncrossy creator here. Yes, I design each by hand using a highly custom editor that I built to help with many of the common challenges. People keep asking me if it's a job that AI could do, but I think it would be really hard to get right. There's a lot of craft in building the shape of the puzzle and picking words that allow as many spaces as possible to get filled in.
That's super cool! Thanks for explaining. Just to clarify myself - when I was thinking of automation I was not thinking of AI, or at least not how its popularly understood these days. I was thinking more like how 2D mazes can be automatically made by non-AI methods. Uncrossy would need a kind of constraint solver that can navigate words lexically and the spatial intersection of words (and their possible slides). I'm certainly not telling you anything you don't know. There are some algorithms for these (general) kinds of constraint satisfaction problems and they long pre-date "AI", but I think the application to uncrossy and refinement would be quite challenging.
Of the various internet .+P, NTP is one I never learned about as a student, so now I'm looking at its web page [1] by its creator David L. Mills (1938-2024). I've found one video of him giving a retrospective of his extensive internet work; he talks about NTP at 34:51 [2] and later at 56:26 [3].
In [3] he mentions that one can use NTP to observe frequency deviations and use it as an early warning system for fire and AC failure. That really intrigues me. Can you actually? Has this ever been implemented?
Oscillators of all kinds are temperature dependent.
That's why the most stable ones are insulated and ovenized[1].
So an AC failure which would lead to higher room temperatures would lead to stronger or more frequent correction by the NTP client, as the local oscillator would drift more.
Not sure about the fire case though. I mean the same applies there but I'm not imaginative enough to think of a realistic scenario where NTP would be useful for averting a fire.
I knew of some experiments in this space back in the late 1980s or early 1990s - but it was specifically with DECstation hardware that had terrible clocks (not used for alerting, just "this graphs nicely against temperature".) https://groups.csail.mit.edu/ana/Publications/PubPDFs/Greg.T... (PDF) 4.2.1 does talk about explaining local clock frequency changes with office temperature changes (because they overwhelm a clock-aging model) but it doesn't have graphs so perhaps they weren't clear enough to include (or just not relevant enough to Time Surveying.)
Others have more complete answers, but the value for me of learning Prolog (in college) was being awakened to a refreshingly different way of expressing a program. Instead of saying "do this and this and this", you say "here's what it would mean for the program to be done".
Circle K in Chicago has them too. Employees are constantly needed to monitor things at checkout. If items aren't clearly separated (which is easy to do out of haste, not malice) it will under-count. The same cashiers at the same store were much faster at checkout than these machines are.
Thank you for giving the specific name ("Dynamic XFA (XML Form Architecture)") to the kind of PDF that generates the "Please wait" message. I have seen this and never understood how it arises. Chuck Geschke spins in his grave ...
Note: the improved loop in the "1. They are not exact" can easily hang.
If count > 2^24, then the ulp of f is 2, and adding 1.0f leaves f unchanged.
What's wild is that a few lines later he notes how above 2^24 numbers start “jumping” every 2. Ok, but FP are always "jumping", by their ulp, regardless of how big or small they are.
I started work on Teem [1] as a grad student 25 years ago; a coordinated set of C libraries for scientific visualization. It includes the original implementation of the NRRD file format [2]. One of my goals for this summer is to finally finish a version 2, so I try to spend a little time every day whittling down my todo list for that release. Currently fixing some things in the command-line parsing library ("hest"). Hearing about other people's long-standing projects is encouraging.
I keep it now in my office, and once a year I bring to the data visualization class I teach at UChicago, to show how it works, and to show it as an example of a visual device in aid of computational thinking (nomographs being another great example).
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